Latest News:

I will be on leave from CMU, as Visiting Professor of Urban Analytics at New York University's Center for Urban Science and Progress, from 7/1/2016-6/30/2018. I will continue to direct the EPD Lab and advise my current students, but am not accepting new CMU students while on leave.

Our rodent prevention work was recently featured in an article on CityLab.
According to the article, "The city of Chicago is still running Neill's predictive analytics approach and has
touted that it's 20 percent more effective than the traditional method of baiting rats after they've been
discovered."

Our crime prediction work with the Pittsburgh Bureau of Police was featured in an editorial in the 30 Sep 2016 issue of
Science.

Our comprehensive review article, "Youth violence: what we know and what we need to know", was featured in
a press release by the
American Psychological Association. The article was published in the January 2016 issue of the APA's
flagship journal, American Psychologist, and is available here.

We are grateful to the Richard King Mellon Foundation for their support of our project, "Urban Predictive Analytics for a Safer and Cleaner Pittsburgh", as part of the award, "Metro21: Knowledge-Powered Pittsburgh to Improve Urban Quality of Life". More information on this project is available here.

What can machine learning do for the healthcare industry? Here are some examples from my
own work, presented as part of the UPMC Enterprises "Inspiration, Innovation, and Excellence" talk series. And here is a related summary of our lab's recent work and ongoing projects in
healthcare and other domains.

Teaching:

I am currently teaching four courses at the Heinz College. Course
descriptions, sample syllabi, and lecture slides can be obtained by
clicking on the course names below, and current course materials are
available on Blackboard.

I am also teaching two Ph.D.-level seminar courses, intended for doctoral
students (and qualified master's students) from Heinz College, the Machine
Learning Department, and other university departments who wish to engage
in cutting-edge research at the intersection of machine learning and
public policy. The Research Seminar in
Machine Learning and Policy (90-904, cross-listed in MLD as 10-830)
is a half-semester course which covers a broad range of MLP topics.
Special Topics in Machine Learning and Policy (90-921, cross-listed in MLD
as 10-831) is a half-semester course which will explore a single MLP topic
in detail. Topics covered include Event and Pattern Detection
(Spring 2010 and Spring 2014), Machine Learning for the Developing World (Spring
2011), Harnessing the Wisdom of Crowds (Spring 2012), and Mining Massive Datasets (Spring 2013).

I also direct the Joint Ph.D. Program in Machine Learning and
Public Policy, offered jointly by the Heinz College and Machine Learning
Department at CMU. Information about this program is available
here.

Research:

My research is focused on novel statistical and computational methods for discovery of emerging events and other relevant patterns in complex
and massive datasets, applied to real-world policy problems ranging from medicine and public health to law enforcement and security.
Application areas include disease surveillance (e.g., using electronically available public health data such as hospital visits and
medication sales to automatically identify and characterize emerging outbreaks), law enforcement (e.g., detection and prediction of
crime patterns using offense reports and 911 calls), health care (e.g., detecting anomalous patterns of care which significantly
impact patient outcomes), and urban analytics (e.g., helping city governments to predict and proactively respond to emerging patterns
of citizen needs).

I also gratefully acknowledge funding support from a UPMC Healthcare Technology Innovation Grant, NSF Graduate Research Fellowship, the John
D. and Catherine T. MacArthur Foundation, Richard King Mellon Foundation, and Disruptive Health Technology Institute. Any opinions, findings, and conclusions or
recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science
Foundation, UPMC, DHTI, Richard King Mellon Foundation, or MacArthur Foundation.